Understanding Facebook to Instagram migration: a push-pull migration model perspective

Abstract

Purpose Emerging social networking sites (SNSs) are less advantageous than leading SNSs in attracting users. They might stand a better chance if they know what users want. The purpose of this paper is to study factors that urge Facebook users to switch to Instagram to reveal how latecomers in the SNSs circuit can win the hearts of users. Design/methodology/approach This study proposes an SNS switching framework that is adapted from the demographic Push-Pull (PP) migration model to investigate users’ switching intention. Structural equation modeling was applied to analyze the data collected from 260 Instagrammers who all had experience using Facebook before moving on Instagram. Findings Results show that socializing and system quality of the SNS negatively affect users’ switching intention, while attractiveness of the alternative, peer influence and critical mass do the opposite. Surprisingly, enjoyment is not associated with switching intention. Research limitations/implications SNSs switching may not mean a complete abandonment of previous SNSs. In many occasions, users simply become less active in one SNS and more active in other SNSs. The PP migration model provides a useful tool to understand the patterns as well as competing forces that influence the migration of SNS users, pushing them away or pulling them to new alternative sites. Specifically, pulling demonstrates to be a stronger influence than pushing. Practical implications This study suggests that SNS operators should satisfy users’ needs for socializing, maintain high system quality, provide peer influence tools and create their own attractive features, in order to retain existing customers or induce new users to switch. Originality/value This is one of the earlier empirical studies to investigate users’ switching intention from Facebook to Instagram with a valid sample. In addition, the present study approaches pull and push effects by multiple constructs, providing a clearer picture of what constitutes the pull and push forces.

A Study on Traditional Teaching Method Transferring to E-Learning Under the Covid-19 Pandemic: From Chinese Students' Perspectives

“…This study includes several major contributions. First, from a theoretical perspective, previous switching behavior studies on PPM have tended to discuss long-term-oriented migration behavior, such as switches from Instagram to Facebook (Hou and Shiau, 2020 ), from E-commerce to M-Shopping or social commerce (Chang et al, 2017 ; Li and Ku, 2018 ), or from mobile instant messaging (Sun et al, 2017 ) or cloud storage services (Wu et al, 2017 ; Cheng et al, 2019 ). This study investigated migration behavior during the emergency management implemented by Chinese universities under the impact of the COVID-19 pandemic.…”

Factors Influence Students’ Switching Behavior to Online Learning under COVID-19 Pandemic: A Push–Pull–Mooring Model Perspective

“…Push effect includes negative influences that drive users to leave existing services. Moreover, several studies have demonstrated the influence of push effects in switching to a new service (Hou & Shiau, 2020 ; Jung et al, 2017 ; Li & Ku, 2018 ). Learning convenience, quality of service, and security risk have proven to be push-oriented effects in previous studies (Chen & Keng, 2019 ; Cheng et al, 2019 ; Liao et al, 2019 ).…”

“…How to think about switching intention from switching classrooms to online learning platforms? Additionally, regarding the switching intentions of the users, previous studies mainly used PPM to explain the switching behavior (Hou & Shiau, 2020 ; Xu et al, 2014 ). Based on Bansal et al ( 2005 ) pointed out, the PPM model structures a list of predictor variables into theoretically defined effect categories.…”

“…Previous research that used the PPM model to discuss related issues shows that the main push and pull factors are dissatisfaction (Tang & Chen, 2020 ; Xu et al, 2014 ; Chang et al, 2014 ; Zhang et al, 2012 ; Su and Wu, 2020 ), and alternative attractiveness, respectively (Tang & Chen, 2020 ; Susanty et al, 2020 ; Jung et al, 2017 ; Su and Chen, 2020 ). However, switching intention is still affected by many factors (Hou & Shiau, 2020 ).…”

Predictors of Corporate Reputation through Service Quality: A Study of Air Asia Customers Perspectives

“…Moreover, multicollinearity occurs when there are two constitutes or more variables are highly correlated. Evaluation of multicollinearity can be done by assessing variance inflation factors (VIF) (Hou & Shiau, 2019). If the VIF value is greater than 10 and the tolerance level greater than 0.10, then there is an issue with the multicollinearity (Mayr & Zins, 2012).…”